Early font generation methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]} aim at learning a mapping function between source fonts and target fonts. When new font references are introduced, these methods use hundreds of reference glyphs to fine-tune the original mapping function. zi2zi [1]} and Rewrite [2]} train GANs in a supervised way with one-hot style labels. AGEN[3]} proposes an model based on the auto encoder to transfer standard font images to calligraphy images. HAN [5]} designs skip connection and hierarchical loss functions to improve zi2zi's generation performance. These methods require paired data to train a mapping function, e.g,775 for [7]}. Other methods [8]}, [11]} focus on unpaired data, and use them for style extraction. Although these many-shot font generation methods have achieved remarkable performance, it is still a laborious task to collect hundreds of references for the fine-tuning process, especially when the reference font library is glyph-rich.
